Personal audio system using processing parameters learned from user feedback

ABSTRACT

Learning personal audio systems and methods are disclosed. A learning personal audio system characterizes the digitized ambient sound so as to generate feature data for the digitized ambient sound, instructs a network interface to transmit the feature data to the remote server, requests one or more appropriate sound profiles from the remote server based upon the feature data, receives one or more selected sound profiles, selected by the remote server based upon the feature data, and initiates processing of the digitized ambient sound based upon the one or more selected sound profiles received from the remote server to generate digitized processed sound. The one or more selected sound profiles for the learning personal audio system are based upon sound profiles which are manually selected by a plurality of other users of personal audio systems similar to the learning personal audio system.

RELATED APPLICATION INFORMATION

This patent is a contuation-in-part of patent application Ser. No. 14/819,298, entitled “Active Acoustic Filter with Automatic Selection Of Filter Parameters Based on Ambient Sound,” filed Aug. 5, 2015, which is a continuation-in-part of U.S. patent application Ser. No. 14/681,843, entitled “Active Acoustic Filter with Location-Based Filter Characteristics,” filed Apr. 8, 2015, which claims priority from provisional patent application 61/976,794, entitled “Digital Acoustical Filters for Use in Human Ears and Method for Using Same”, filed Apr. 8, 2014, all of which are incorporated herein by reference.

NOTICE OF COPYRIGHTS AND TRADE DRESS

A portion of the disclosure of this patent document contains material which is subject to copyright protection. This patent document may show and/or describe matter which is or may become trade dress of the owner. The copyright and trade dress owner has no objection to the facsimile reproduction by anyone of the patent disclosure as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright and trade dress rights whatsoever.

BACKGROUND

1. Field

This disclosure related to digital active filters for use in a listener's ear to modify ambient sound to suit the listening preferences of the listener.

2. Description of the Related Art

Humans' perception to sound varied with both frequency and sound pressure level (SPL). For example, humans do not preceive lower and higher frequency sounds as well as they perceive sounds at midrange frequencies between 500 Hz and 6,000 Hz. Further, human hearing is more responsive to sound at high frequencies compared to low frequencies.

There are many situations where a listener may desire attenuation of ambient sound at certain frequencies, while allowing ambient sound at other frequencies to reach their ears. For example, at a concert, cpncert goers might want to enjoy the music, but also be protected from high levels of mid-range sound frequencies that cause damage to a person's hearing. On an airplane, apssengers might wish to block out the roar of the engine, but not conversion. At a sports event, fans might desire to hear the action of the game, but receive protection from the roar of the crowd. At a construction site, a worker may need to hear nearby sounds and voices for safety and to enable the construction to continue, but may wish to protect his or her ears from sudden, loud noises of crashes or large moving equipment. Further, a user may wish to engage in conversation and other activities without being interrupted or impaired by annoyance noises such as sounds of engines or motors, crying babies, and sirens. These are just a few common examples where people wish to hear some, but not all, of the sounds in their environment.

In addition to receiving protection from unpleasant or dangerously loud sound levels, listeners may wish to augment the ambient sound by amplification of certain frequencies, combining ambient sound with a secondary audio feed, equalization (modifying ambient sound by adjusting the relative loudness of various frequencies), noise reduction, addition of white or pink noise to mask annoyances, echo cancellation, and addition of echo or reverberation. For example, at a concert, audience members may wish to attenuate certain frequencies of the music, but amplify other frequencies (e.g., the bass). People listening to music at home may wish to have a more “concert-like” experience by adding reverberation to the ambient sound. At a sports event, fans may wish to attenuate ambient crowd noise, but also receive an audio feed of a sportscaster reporting on the event. Similarly, people at a mall may wish to attenuate the ambient noise, yet receive an audio feed of advertisements targeted to their location. These are just a few examples of peoples' listening preferences.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a sound processing system.

FIG. 2 is block diagram of an active acoustic filter.

FIG. 3 is a block diagram of a personal computing device.

FIG. 4 is a functional block diagram of a portion of a personal audio system.

FIG. 5 is a block diagram of a sound knowledgebase.

FIG. 6 is a flow chart of a method for processing sound using collective feedforward.

FIG. 7 is a flow chart of a method for extracting features.

FIG. 8 is a flow chart of another method for processing sound using collective feedforward.

Throughout this description, elements appearing in figures are assigned three-digit reference designators. An element not described in conjunction with a figure has the same characteristics and function as a previously-described element having the same reference designator.

DETAILED DESCRIPTION

Description of Apparatus

Referring now to FIG. 1, a sound processing system 100 includes at least one personal audio system 140 and a sound knowledgebase 150 within a cloud 130. The sound processing system 100 may include a large plurality of personal audio systems. In this context, the term “cloud” means a network and all devices that may be accessed by the personal audio system 140 via the network. The term “device” means an apparatus capable of communicating via a network or other digital communications link. Examples of devices include cellular phones, computing tablets, portable computers, desk-top computers, servers, network storage units, and peripheral devices. The cloud 130 may be a local area network, wide area network, a virtual network, or some other form of network together with all devices connected to the network. The cloud 130 may be or include the Internet. The term “knowledgebase” connotes a device that not only stores data, but also learns and stores other knowledge derived from the data. The sound knowledgebase 150 may include a database and one or more servers. The sound knowledgebase 150 will be described in further detail during the discussion of FIG. 5.

The personal audio system 140 includes left and right active acoustic filters 110L, 110R and a personal computing device 120. While the personal computing device 120 is shown in FIG. 1 as a smart phone, the personal computing device 120 may be a smart phone, a desktop computer, a mobile computer, a tablet computer, or any other computing device that is capable of performing the processes described herein. The personal computing device 120 may include one or more processors and memory configured to execute stored software instructions to perform the processes described herein.

The active acoustic filters 110L, 110R communicate with the personal computing device 120, such as via a first wireless communications link 112. While only a single first wireless communications link 112 is shown in FIG. 1, each active acoustic filter 110L, 110R may communicate with the personal computing device 120 via separate wireless communication links. The first wireless communications link 112 may use a limited-range wireless communications protocol such as Bluetooth®, WiFi®, ZigBee®, or some other wireless Personal Area Network (PAN) protocol. Alternatively, there may be a direct connection from the personal computing device 120 to one of the active acoustic filters, and an indirect connection to the other active acoustic filter. The indirect connection may be through the direct connection, wherein the directly connected active acoustic filter acts as a relay to the indirectly connected active acoustic filter.

The personal computing device 120 communicates with the cloud 130, for example, via a second communications link 122. In particular, the personal computing device 120 may include one or more processors and memory configured to execute stored software instructions to perform the processes described herein.

The active acoustic filters 110L, 110R communicate with the personal computing device 120, such as via a first wireless communications link 112. While only a single first wireless communications link 112 is shown in FIG. 1, each active acoustic filter 110L, 110R may communicate with the personal computing device 120 via separate wireless communication links. The first wireless communications link 112 may use a limited-range wireless communications protocol such as Bluetooth®, WiFi®, ZigBee®, or some other wireless Personal Area Network (PAN) protocol. Alternatively, there may be a direct connection from the personal computing device 120 to one of the active acoustic filters, and an indirect connection to the other active acoustic filter. The indirect connection may be through the direct connection, wherein the directly connected active acoustic filter acts as a relay to the indirectly connected active acoustic filter.

The personal computing device 120 communicates with the cloud 130, for example, via a second communications link 122. In particular, the personal computing device 120 may communicate with the sound knowledgebase 150 within the cloud 130 via the second communications link 122. The second communications link 122 may be a wired connection or may be a wireless communications link using, for example, the WiFi® wireless communications protocol, a mobile telephone data protocol, or another wireless communications protocol.

Optionally, the acoustic filters 110L, 110R may communicate directly with the cloud 130 via a third wireless communications link 114. The third wireless communications link 114 may be an alternative to, or in addition to, the first wireless communications link 112. The third wireless connection 114 may use, for example, the WiFi® wireless communications protocol, or another wireless communications protocol. The acoustic filters 110L, 110R may communicate with each other via a fourth wireless communications link (not shown). This fourth wireless communication link may provide an indirect connection of one active acoustic filter to the cloud 130 through the other active acoustic filter.

FIG. 2 is block diagram of an active acoustic filter 200, which is representative of the active acoustic filter 110L and the active acoustic filter 110R. The active acoustic filter 200 includes at least one microphone 210, a preamplifier 215, an analog-to-digital (A/D) converter 220, a wireless interface 225, a processor 230, a memory 235, a digital-to-analog (D/A) converter 240, an amplifier 245, a speaker 250, and a battery (not shown), all of which may be contained within a housing 290. The active acoustic filter 200 receives ambient sound 205 and outputs personal sound 255. In this context, the term “sound” refers to acoustic waves propagating in air. “Personal sound” means sound that has been processed, modified, or tailored in accordance with a user's personal preferences. The term “audio” refers to an electronic representation of sound, which may be an analog signal or digital data.

The housing 290 is configured to interface with a user's ear by fitting in, on, or over the user's ear such that the ambient sound 205 is mostly excluded from reaching the user's ear canal and the personal sound 255 generated by the active acoustic filter is provided directly into the user's ear canal.

The housing 290 has at least a first aperture 292 for accepting the ambient sound 205 and a second aperture 294 to allow the personal sound 255 to be output into the user's outer ear canal. The housing 290 may have more than one aperture for accepting ambient sound, each of which may be coupled to a separate microphone. The housing 290 may be, for example, an earbud housing. The term “earbud” means an apparatus configured to fit, at least partially, within and be supported by a user's ear. An earbud housing typically has a portion that fits within or against the user's outer ear canal. An earbud housing may have other portions that fit within the concha or pinna of the user's ear.

The microphone 210 converts the ambient sound 205 into an electrical signal that is amplified by preamplifier 215 and converted into an ambient audio stream 222 by A/D converter 220. In this context, the term “stream” means a sequence of digital samples. The “ambient audio stream” is a sequence of digital samples representing the ambient sound received by the active acoustic filter 200. The ambient audio stream 222 is processed by processor 230 to provide a personal audio stream 232. The processing performed by the processor 230 will be discussed in more detail subsequently. The personal audio stream 232 is converted into an analog signal by D/A converter 240. The analog signal output from D/A converter 240 is amplified by amplifier 245 and converted into personal sound 255 by speaker 250.

The microphone 210 may be one or more transducers for converting sound into an electrical signal that is sufficiently compact for use within the housing 290. The preamplifier 215 is configured to amplify the electrical signal output from the microphone 210 to a level compatible with the input of the A/D converter 220. The preamplifier 215 may be integrated into the A/D converter 220, which, in turn, may be integrated with the processor 230. In the situation where the active acoustic filter 200 contains more than one microphone, a separate preamplifier may be provided for each microphone.

The A/D converter 220 digitizes the output from preamplifier 215, which is to say converts the output from preamplifier 215 into a series of digital ambient audio samples at a rate at least twice the highest frequency present in the ambient sound. For example, the A/D converter may output the ambient audio stream 222 in the form of sequential audio samples at rate of 40 kHz or higher. The resolution of the ambient audio stream 222 (i.e., the number of bits in each audio sample) may be sufficient to minimize or avoid audible sampling noise in the processed output sound 255. For example, the A/D converter 220 may output an ambient audio stream 222 having 12 or more bits of amplitude resolution. In the situation where the active acoustic filter 200 contains more than one microphone with respective preamplifiers, the outputs from the preamplifiers may be digitized separately, or the outputs of some or all of the preamplifiers may be combined prior to digitization.

The wireless interface 225 provides digital acoustic filter 200 with a connection to one or more wireless networks 295 using a limited-range wireless communications protocol such as Bluetooth®, WiFi®, ZigBee®, or other wireless personal area network protocol. The wireless interface 225 may be used to receive data such as parameters for use by the processor 230 in processing the ambient audio stream 222 to produce the personal audio stream 232. The wireless interface 225 may be used to receive a secondary audio feed. The wireless interface 225 may be used to export the personal audio stream 232, which is to say transmit the personal audio stream 232 to a device external to the active acoustic filter 200. The external device may then, for example, store and/or publish the personal audio stream, for example via social media.

The processor 230 may include one or more processor devices such as a microcontroller, a microprocessor, and/or a digital signal processor. The processor 230 can include and/or be coupled to the memory 235. The memory 235 may store software programs, which may include an operating system, for execution by the processor 230. The memory 235 may also store data for use by the processor 230. The data stored in the memory 235 may include, for example, digital sound samples and intermediate results of processes performed on the ambient audio stream 222. The data stored in the memory 235 may also include a user's listening preferences, and/or rules and parameters for applying particular processes to convert the ambient audio stream 222 into the personal audio stream 232. The memory 235 may include a combination of read-only memory, flash memory, and static or dynamic random access memory.

The D/A converter 240 converts the personal audio stream 232 from the processor 230 into an analog signal. The processor 230 outputs the personal audio stream 232 as a series of samples typically, but not necessarily, at the same rate as the ambient audio stream 222 is generated by the A/D converter 220. The analog signal output from the D/A converter 240 is amplified by the amplifier 245 and converted into personal sound 255 by the speaker 250. The amplifier 245 may be integrated into the D/A converter 240, which, in turn, may be integrated with the processor 230. The speaker 250 can be any transducer for converting an electrical signal into sound that is suitably sized for use within the housing 290.

A battery or other power supply (not shown) provides power to various elements of the active acoustic filter 200. The battery may be, for example, a zinc-air battery, a lithium ion battery, a lithium polymer battery, a nickel cadmium battery, or a battery using some other technology.

The depiction in FIG. 2 of the active acoustic filter 200 as a set of functional blocks or elements does not imply any corresponding physical separation or demarcation. All or portions of one or more functional elements may be located within a common circuit device or module. Any of the functional elements may be divided between two or more circuit devices or modules. For example, all or portions of the analog-to-digital (A/D) converter 220, the processor 230, the memory 235, the analog signal by digital-to-analog (D/A) converter 240, the amplifier 245, and the wireless interface 225 may be contained within a common signal processor circuit device.

FIG. 3 is a block diagram of an exemplary personal computing device 300, which may be the personal computing device 120. As shown in FIG. 3, the personal computing device 300 includes a processor 310, memory 320, a user interface 330, a communications interface 340, and an audio interface 350. Some of these elements may or might not be present, depending on the implementation. Further, although these elements are shown independently of one another, each may, in some cases, be integrated into another.

The processor 310 may be or include one or more microprocessors, microcontrollers, digital signal processors, application specific integrated circuits (ASICs), or a system-on-a-chip (SOCs). The memory 320 may include a combination of volatile and/or non-volatile memory including read-only memory (ROM), static, dynamic, and/or magnetoresistive random access memory (SRAM, DRM, MRAM, respectively), and nonvolatile writable memory such as flash memory.

The memory 320 may store software programs and routines for execution by the processor. These stored software programs may include an operating system such as the Apple® MacOS or IOS operating systems or the Android® operating system. The operating system may include functions to support the communications interface 340, such as protocol stacks, coding/decoding, compression/decompression, and encryption/decryption. The stored software programs may include an application or “app” to cause the personal computing device to perform portions of the processes and functions described herein.

The user interface 330 may include a display and one or more input devices such as a touch screen.

The communications interface 340 includes at least one interface for wireless communication with external devices. The communications interface 340 may include one or more of a cellular telephone network interface 342, a wireless local area network (LAN) interface 344, and/or a wireless personal area network (PAN) interface 336. The cellular telephone network interface 342 may use one or more cellular data protocols. The wireless LAN interface 344 may use the WiFi® wireless communication protocol or another wireless local area network protocol. The wireless PAN interface 346 may use a limited-range wireless communication protocol such as Bluetooth®, Wi-Fi®, ZigBee®, or some other public or proprietary wireless personal area network protocol. When the personal computing device 300 is deployed as part of a personal audio system, such as the personal audio system 140, the wireless PAN interface 346 may be used to communicate with the active acoustic filter devices 110L, 110R. The cellular telephone network interface 342 and/or the wireless LAN interface 344 may be used to communicate with the cloud 130.

The communications interface 340 may include radio-frequency circuits, analog circuits, digital circuits, one or more antennas, and other hardware, firmware, and software necessary for communicating with external devices. The communications interface 340 may include one or more processors to perform functions such as coding/decoding, compression/decompression, and encryption/decryption as necessary for communicating with external devices using selected communications protocols. The communications interface 340 may rely on the processor 310 to perform some or all of these function in whole or in part.

The audio interface 350 may be configured to both input and output sound. The audio interface 350 may include more or more microphones, preamplifiers and A/D converters that perform similar functions as the microphone 210, preamplifier 215 and A/D converter 220 of the active acoustic filter 200. The audio interface 350 may include more or more D/A converters, amplifiers, and speakers that perform similar functions as the D/A converter 240, amplifier 245 and speaker 250 of the active acoustic filter 200.

The personal computing device 300 may be configured to perform geo-location, which is to say to determine its own location. Geo-location may be performed, for example, using a Global Positioning System (GPS) receiver or by some other method.

FIG. 4 shows a functional block diagram of a portion of an exemplary personal audio system 400, which may be the personal audio system 140. The personal audio system 400 includes one or two active acoustic filters, such as the active acoustic filters 110L, 110R, and a personal computing device, such as the personal computing device 120. The functional blocks shown in FIG. 4 may be implemented in hardware, by software running on one or more processors, or by a combination of hardware and software. The functional blocks shown in FIG. 4 may be implemented within the personal computing device, or within one or both active acoustic filters, or may be distributed between the personal computing device and the active acoustic filters.

The personal audio system 400 includes an audio processor 410, a controller 420, a dataset memory 430, an audio snippet memory 440, a user interface 450 and a geo-locator 460. The audio processor 410 and/or the controller 420 may include their own memory, which is not shown, for storing program instructions, intermediate results, and other data.

The audio processor 410 may be or include one or more microprocessors, microcontrollers, digital signal processors, application specific integrated circuits (ASICs), or a system-on-a-chip (SOCs). The audio processor 410 may be located within an active acoustic filter, within the personal computing device, or may be distributed between a personal computing device and one or two active acoustic filters.

The audio processor 410 receives and processes a digital ambient audio stream, such as the ambient audio stream 222, to provide a personal audio stream, such as the personal audio stream 232. The audio processor 410 may perform processes including filtering, equalization, compression, limiting, and/or other processes. Filtering may include high-pass, low-pass, band-pass, and band-reject filtering. Equalization may include dividing the ambient sound into a plurality of frequency bands and subjecting each of the bands to a respective attenuation or gain. Equalization may be combined with filtering, such as a narrow band-reject filter to suppress a particular objectionable component of the ambient sound. Compression may be used to alter the dynamic range of the ambient sound such that louder sounds are attenuated more than softer sounds. Compression may be combined with filtering or with equalization such that louder frequency bands are attenuated more than softer frequency bands. Limiting may be used to attenuate louder sounds to a predetermined loudness level without attenuating softer sounds. Limiting may be combined with filtering or with equalization such that louder frequency bands are attenuated to a defined level while softer frequency bands are not attenuated or attenuated by a smaller amount.

The audio processor 410 may also add echo or reverberation to the ambient audio stream. The audio processor 410 may also detect and cancel an echo in the ambient audio stream. The audio processor 410 may further perform noise reduction processing.

The audio processor 410 may receive a secondary audio stream. The audio processor 410 may incorporate the secondary audio stream into the personal audio stream. The secondary audio stream may be added to the ambient audio stream before processing, after all processing of the ambient audio stream is performed, or at an intermediate stage in the processing of the ambient audio stream. The secondary audio stream might not be processed, or may be processed in the same manner as or in a different manner than the ambient audio stream.

The audio processor 410 may process the ambient audio stream, and optionally the secondary audio stream, in accordance with an active processing parameter set 425. The active processing parameter set 425 may define the type and degree of one or more processes to be performed on the ambient audio stream and, when desired, the secondary audio stream. The active processing parameter set may include numerical parameters, filter models, software instructions, and other information and data to cause the audio processor to perform desired processes on the ambient audio stream. The extent and format of the information and data within active processing parameter set 425 may vary depending on the type of processing to be performed. For example, the active processing parameter set 425 may define filtering by a low pass filter with a particular cut-off frequency (the frequency at which the filter start to attenuate) and slope (the rate of change of attenuation with frequency) and/or compression using a particular function (e.g. logarithmic). For further example, the active processing parameter set 425 may define the plurality of frequency bands for equalization and provide a respective attenuation or gain for each frequency band. In yet another example, the processing parameters may define a delay time and relative amplitude of an echo to be added to the digitized ambient sound.

The audio processor 410 may receive the active processing parameter set 425 from the controller 420. The controller 420, in turn, may obtain the active processing parameter set 425 from the user via the user interface 450, from the cloud (e.g. from the sound knowledgebase 150 or another device within the cloud), or from the dataset memory 430 within the personal audio system 400.

The dataset memory 430 may store one or more processing parameter sets 432, which may include a copy of the active processing parameter set 425. The dataset memory 430 may store dozens or hundreds or an even larger number of processing parameter sets 432. Each processing parameter set 432 may be associated with at least one indicator, where an “indicator” is data indicating conditions or circumstances where the associated processing parameter set 432 is appropriate for selection as the active processing parameter set 425. The indicators associated with each processing parameter set 432 may include one or more of a location 434, an ambient sound profile 436, and a context 438. The combination of a processing parameter set and its associated indicators is considered a “dataset”.

The dataset memory 430 may include processing parameter sets 432 that have been obtained from a remote server, such as the sound knowledgebase 150. These processing parameter sets 432 may be downloaded before a user leaves a wireless network and stored for later use. This pre-loading may take into account activities currently on a user of the personal audio system's 400 calendar, schedule, email, texts or other information indicating a location or event which the user will attend. In so doing, the personal audio system 400 may dynamically decide which processing parameter sets 432 are relevant.

Alternatively, the processing parameter sets 432 dataset memory 430 may downloaded dynamically based upon audio characteristics of ambient audio, as detected by the personal audio system 400, a current (or future) location for the personal audio system 400 as determined by geo-location capabilities within the personal audio system 400 (discussed below), or based upon the context in which the personal audio system 400 finds itself. In order to obtain the processing parameter sets 432, the personal audio system 400 may use its own wireless interface 225 (FIG. 2) directly or may communicate through the communications interface 340 of the personal computing device 300 (FIG. 3) to thereby access a remote repository of processing parameter sets, such as the sound knowledgebase 150 (FIG. 1).

The downloaded processing parameter sets may be obtained in real-time from the sound knowledebase 150 or a similar database as a user moves into a particular location, is about to move into a particular location, begins hearing ambient sound with certain characteristics, the ambient sound characteristics change, or as the sound knowledgebase's 150 most-commonly-used or most-appropriate for the environment or recommended processing parameter set changes, for example, due to user feedback from other users of personal audio systems similar to the personal audio system.

Locations 434 may be associated with none, some, or all of the processing parameter sets 432 and stored in the dataset memory 430. Each location 434 defines a geographic position or limited geographic area where the associated set of processing parameters 432 is appropriate. A geographic position may be defined, for example, by a street address, longitude and latitude coordinates, GPS coordinates, or in some other manner. A geographic position may include fine-grained information such as a floor or room number in a building. A limited geographic area may be defined, for example, by a center point and a radius, by a pair of coordinates identifying diagonal corners of a rectangular area, by a series of coordinates identifying vertices of a polygon, or in some other manner.

Ambient sound profiles 436 may be associated with none, some, or all of the processing parameter sets 432 and stored in the dataset memory 430. Each ambient sound profile 436 defines features and characteristics of an ambient sound environment in which the associated processing parameter set 432 is appropriate. Each ambient sound profile 436 may define the features and characteristics of the ambient sound environment by a finite number of numerical values. For example, an ambient profile may include numerical values for some or all of an overall loudness level, a normalized or absolute loudness of predetermined frequency bands, a spectral envelope shape, spectrographic features such as rising or falling pitch, frequencies and normalized or absolute loudness levels of dominant narrow-band sounds, an indicator of the presence or absence of odd and/or even harmonics, a normalized or absolute loudness of noise, a low frequency periodicity (e.g. the “beat” when the ambient sound includes music), and numerical values quantifying other features and/or characteristics.

Contexts 438 may be associated with none, some, or all of the processing parameter sets 432 and stored in the dataset memory 430. Each context 438 is a descriptive name of an environment or situation in which the associated processing parameter set 432 is appropriate. Examples of contexts include “airplane cabin,” “subway,” “urban street,” “siren,” and “crying baby.” A context is not necessarily associated with a specific geographic location, but may be associated with a generic location type such as, for example, “airplane,” “subway,” and “urban street.” The phrase “location types”, as used herein are metadata tags for locations in the sound knowledgebase 150 that are associated with the physical characteristics of a location. For example, a location with physical characteristics of an ampitheater may be associated using metadata tags with the location type “ampitheater”. In the absence of specific processing parameters related to a location, a personal audio system may utilize appropriate processing parameters for the location type “ampitheater”. Similar location types, and associated processing parameters, may be defined for other location types such as stadium, indoor basketball court, theater, tennis court, outdoor concert venue, bar, subway, airplane, restaurant, opera house, and other similar location types. Location types may be pre-determined at manufacture, may be introduced through updates from a manufacturer or seller, may be created over time through collective feedforward activity of a plurality of users, or may be defined by an individual user.

A context may be associated with a type of ambient sound such as, for example, “siren,” “crying baby,” and “rock concert.” A context may be associated with one or more sets of processing parameters. When a context is associated with multiple processing parameter sets 432, selection of a particular processing parameter set may be based on location or ambient sound profile. For example, “siren” may be associated with a first set of processing parameters for locations in the United States and a different set of processing parameters for locations in Europe.

The controller 420 may select a processing parameter set 432 for use as the active processing parameter set 425 based on location, ambient sound profile, context, or a combination thereof. Retrieval of a processing parameter set 432 may be requested by the user via the user interface 450. Alternatively or additionally, retrieval of a processing parameter set 432 may be initiated automatically by the controller 420.

For example, the controller 420 may include a profile developer 422 to analyze the ambient audio stream to develop a current ambient sound profile. In this case, the controller 420 compares the current ambient sound profile with a stored prior ambient sound profile. When the current ambient sound profile is judged, according to first predetermined criteria, to be substantially different from the prior ambient sound profile, the controller 420 initiates retrieval of a new processing parameter set 432.

The personal audio system 400 may contain a geo-locator 460. The geo-locator 460 determines a geographic location of the personal audio system 400 using GPS, cell tower triangulation, or some other method. As described in co-pending application Ser. No. 14/681,843, entitled “Active Acoustic Filter with Location-Based Filter Characteristics,” the controller 420 may compare the geographic location of the personal audio system 400, as determined by the geo-location 460, with location indicators 434 stored in the dataset memory 430. When one of the location indicators 434 matches, according to second predetermined criteria, the geographic location of the personal audio system 400, the associated processing parameter set 432 may be retrieved and provided to the audio processor 410 as the active processing parameter set 425.

As described in co-pending application Ser. No. 14/819,298, entitled “Active Acoustic Filter with Automatic Selection of Filter Parameters Based on Ambient Sound,” the controller may select a processing parameter set 432 based on the ambient sound. The controller 420 may compare the profile of the ambient sound, as determined by the profile developer 422, with profile indicators 436 stored in the dataset memory 430. When one of the profile indicators 436 matches, according to third predetermined criteria, the profile of the ambient sound, the associated processing parameter set 432 may be retrieved and provided to the audio processor 410 as the active processing parameter set 425.

In some circumstances, for example upon user request or when a matching location or profile is not found in the dataset memory 430, the controller 420 may present a list of the contexts 438 on a user interface 450. A user may then manually select one of the listed contexts and the associated processing parameter set 432 may be retrieved and provided to the audio processor 410 as the active processing parameter set 425. For example, assuming the user interface includes a display with a touch screen, the list of contexts may be displayed on the user interface as array of soft buttons. The user may then select one of the contexts by pressing the associated button.

Datasets (i.e., processing parameter sets 432 and associated indicators 434, 436, 438) may be entered into the dataset memory 430 in several ways. Datasets may have been stored in the dataset memory 430 during manufacture of the personal audio system 400. Datasets may have been stored in the dataset memory 430 during installation of an application or “app” on the personal computing device that is a portion of the personal audio system.

Additional datasets stored in the dataset memory 430 may have been created by the user of the personal audio system 400. For example, an application running on the personal computing device may present a graphical user interface through which the user can select and control parameters to edit an existing processing parameter set and/or to create a new processing parameter set. In either case, the edited or new processing parameter set may be saved in the dataset memory 430 in association with one or more of a current ambient sound profile provided by the profile developer 422, a location of the personal audio system 400 provided by the geo-locator 460, and a context or name entered by the user via the user interface 450. The edited or new processing parameter set to be saved in the dataset memory 430 automatically or in response to a specific user command.

Datasets may be developed by third parties and made accessible to the user of the personal audio system 400, for example, via a network.

Further, datasets may be downloaded from the cloud, such as from the sound knowledgebase 150 in the cloud 130, and stored in the dataset memory 430. For example, newly available or revised processing parameter sets 432 and associated indicators 434, 436, 438 may be pushed from the cloud to the personal audio system 400 automatically. Newly available or revised processing parameter sets 432 and associated indicators 434, 436, 438 may be downloaded from the cloud by the personal audio system 400 at periodic intervals. Newly available or revised processing parameter sets 432 and associated indicators 434, 436, 438 may be downloaded from the cloud by the personal audio system 400 in response to a request from a user.

To support development of new and/or revised processing parameter sets, the personal audio system may upload information, such as to the sound knowledgebase 150 in the cloud 130.

The personal audio system may contain an audio snippet memory 440. The audio snippet memory 440 may be, for example, a revolving or circular buffer memory having a fixed size where the newest data overwrites the oldest data such that, at any given instant, the buffer memory contains a predetermined amount of the most recently stored data. The audio snippet memory 440 may store, for example, the most recent audio stream data for a period of 2 seconds, 5 seconds, 10 seconds, 30 seconds, or some other period.

The audio snippet memory 440 may store a “most recent portion” of an audio stream, where the “most recent portion” is the time period immediately preceding the current time. The audio snippet memory 440 may store the most recent portion of the ambient audio stream input to the audio processor 410 (as shown in FIG. 4), in which case the audio snippet memory 440 may be located within one or both of the active acoustic filters of the personal audio system. The audio snippet memory 440 may store the most recent portion of an audio stream derived from the audio interface 350 in the personal computing device of the personal audio system, in which case the audio snippet memory may be located within the personal computing device 120. In either case, the duration of the most recent portion of the audio stream stored in the audio snippet memory 440 may be sufficient to capture very low frequency variations in the ambient sound such as, for example, periodic frequency modulation of a siren or interruptions in a baby's crying when the baby inhales.

The personal audio system my include an event detector 424 to detect trigger events, which is to say events that trigger uploading the content of the audio snippet memory and associated metadata to the remote device. The event detector 424 may be part of, or coupled to, the controller 420. The event detector 424 may detect events that indicate or cause a change in the active processing parameter set 425 used by the audio processor 410 to process the ambient audio stream. Examples of such events detected by the event detector include the user entering commands via the user interface 450 to modify the active processing parameter set 425 or to create a new processing parameter set; the user entering a command via the user interface 450 to save a modified or new processing parameter set in the dataset memory 430; automatic retrieval, based on location or ambient sound profile, of a selected processing parameter set from the dataset memory 430 for use as the active processing parameter set; and user selection, for example from a list or array of buttons presented on the user interface 450, of a selected processing parameter set from the dataset memory 430 for use as the active processing parameter set. Such events may be precipitated, for example, by a change in the ambient sound environment or by user dissatisfaction with the sound of the personal audio stream obtained with the previously-used active processing parameter set.

Application Ser. No. 14/952,761, “Processing Sound Using Collective Feedforward”, describes a personal audio system that, in response to a trigger event, uploads a most recent audio snippet (i.e., the content of an audio snippet memory) and associated metadata to a remote device. Uploading the most recent audio snippet allows the remote device to perform various analyses to determine the characteristics of the ambient sound immediately prior to the event. However, the most recent audio snippet may contain speech of the user of the personal audio system or other persons. Thus uploading the most recent audio snippet may raise privacy concerns.

The personal audio system 400 may include a feature extractor 426 to extract feature data from the most recent audio snippet stored in the audio snippet memory 440. Feature data that may be extracted from the most recent audio snippet include, for example, data such as means (arithmetic, geometric, harmonic), centroid, variance, standard deviation, spectral skew, kurtosis, spectral envelope shape, spectral rolloff, spread, flatness, spectral flux, Mel frequency cepstral coefficients, pitch, tonal power ratio, harmonic-to-average power ratio, maximum of autocorrelation function, zero crossing rate, RMS power, peak power, crest factor, and/or amplitude/power envelope including estimation of attack/decay rates.

Upon detection of an event by the event detector 424, the feature extractor 426 may extract feature data from the most recent audio snippet stored in the audio snippet memory. The type of audio featurization to apply, and the type and amount of feature data extracted from the most recent audio snippet may depend on the characteristics of the stored audio. Some feature data may be extracted from the entire content of the audio snippet memory 440 and other feature data may be extracted from multiple consecutive time slices of the audio data stored in the audio snippet memory 440. The extracted feature data may then be transmitted to a remote device such as the sound knowledge base in the cloud 130. The feature data transmitted to the remote device may be configured to allow analysis of the ambient sound immediately preceding the event, but not allow reconstruction of speech or recognition of the user or other speakers.

Metadata may be transmitted along with the extracted feature data. The transmitted metadata may include a location of the personal audio system 400, which may be provided by the geo-locator 460. When the trigger event was a user-initiated or automatic retrieval of a selected processing parameter set from the parameter memory, the transmitted metadata may include an identifier of the selected processing parameter set and/or the complete selected processing parameter set. When the trigger event was the user modifying a processing parameter set or creating a new processing parameter set, the transmitted metadata may include the modified or new processing parameter set. Further, the user may be prompted or required to enter, such as via the user interface 450, a context, descriptor, or other tag to be associated with the extracted feature data and transmitted. To preserve user privacy, the transmitted metadata may exclude information that identifies the user or the user's device.

FIG. 5 is a functional block diagram of an exemplary sound knowledgebase 500, which may be the sound knowledgebase 150 within the sound processing system 100. The sound knowledgebase 500 includes a processor 510 coupled to a memory/storage 520 and a communications interface 540. These functions may be implemented, for example, in a single server computer or by one or more real or virtual servers within the cloud.

The processor 510 may be or include one or more microprocessors, microcontrollers, digital signal processors, application specific integrated circuits (ASICs), or a system-on-a-chip (SOCs). The memory/storage 520 may include a combination of volatile and/or non-volatile memory. The memory/storage 520 may include one or more storage devices that store data on fixed or removable storage media. The term “storage media” means a physical object adapted for storing data, which excludes transitory media such as propagating signals or waves. Examples of storage media include magnetic discs and optical discs.

The communications interface 540 includes at least one interface for wired or wireless communications with external devices including a plurality of personal audio systems.

The memory/storage 520 may store a database 522 having a plurality of records. Each record in the database 522 may include a set of audio feature data and associated metadata received from one of a plurality of personal audio systems, such as the personal audio system 400, via the communication interface 540. The memory/storage 520 may also store software programs and routines for execution by the processor. These stored software programs may include an operating system. The operating system may include functions to support the communications interface 540, such as protocol stacks, coding/decoding, compression/decompression, and encryption/decryption. The stored software programs may include a database application (also not shown) to manage the database 522.

The stored software programs in the memory/storage 520 may include a feature data analysis application 524 to analyze audio feature data received from the plurality of personal audio systems. The feature data analysis application 524 may, for example, extract or develop additional data representing the characteristics and features of the ambient sound at the personal audio system that provided the audio feature data. Additional data extracted or developed by feature data analysis application 524 may be stored in the database 522 as part of the record containing the corresponding audio feature data and metadata.

The stored software programs may include a parameter set learning application 526 to learn revised and/or new processing parameter sets from the audio feature data, additional data, and metadata stored in the database 522. The parameter set learning application 526 may use a variety of analytical techniques to learn revised and/or new processing parameter sets. These analytical techniques may be applied to numerical and statistical analysis of audio feature data, additional data, and numerical metadata such as location, date, and time metadata. These analytical techniques may include, for further example, semantic analysis of tags, descriptors, contexts, and other non-numerical metadata. Further, the parameter set learning application 526 may use known machine learning techniques such as neural nets, fuzzy logic, adaptive neuro-fuzzy inference systems, or combinations of these and other machine learning methodologies to learn revised and/or new processing parameter sets.

As an example of a learning process that may be performed by the parameter set learning application 526, the records in the database 522 may be sorted into a plurality of clusters based according to audio feature data, location, tag or descriptor or some other factor. Some or all of these clusters may optionally be sorted into sub-clusters based on another factor. When records are sorted into clusters or sub-clusters based on non-numerical metadata (e.g., tags or descriptors) semantic analysis may be used to combine like metadata into a manageable number of clusters or sub-clusters. A consensus processing parameter set may then be developed for each cluster or sub-cluster. For example, clear outliers may be discarded and the consensus processing parameter set may be formed from the medians or means of processing parameters within the remaining processing parameter sets.

During this learning process by the parameter set learning application 526, at least some of the data used to select the consensus processing parameter set may be received from a plurality of users of personal audio systems 400 (FIG. 4). For example, as a user enters a location or location type,or begins listening to a particular type of ambient sound, or a context; the user may manually select certain processing parameters. For the duration of the time in that location or location type, the duration of that type of ambient sound, or that context, the user may continue using some or all of those processing parameters. The personal audio system 400 for that user may periodically or immediately upload information related to those manually selected processing parameters to the sound knowledgebase 500 through the communications interface 540 for integration by the parameter set learning application 526 into the master dataset memory 528.

These processing parameters are effectively numerical representations of audio processing settings selected by that user. That is, the resulting processing parameters that have been uploaded for integration by the parameter set learning application 526 act as another data point in numerical form that may be integrated directly (e.g. appended to a list of other data points), which may be integrated indirectly (e.g. used to increase or lower an average or a median value for any one of the numerical data points) and on which statistical operations may be performed. The median of data points for a plurality of users is the set of numberical representations of selected processing parameters that is most-often selected by the set of users. The average is the average set of numbers for selected processing parameters.

Thus, the parameter set learning application 526 may, over time, take the the median or average parameter set selected by all of the various users of personal audio systems in similar locations, while hearing similar types of ambient sound, or in similar contexts to thereby create a consensus processing parameter set. Alternatively, the most commonly selected parameters may form the consensus of the processing parameters selected by all users may form the consensus. Processing parameters that are clear outliers, that do not appear to represent intentional selections by users, or that represent non-selections by users, such as when a user changes locations, the ambient audio changes, or the context changes and no change to the processing parameters are manually made, may also be discarded. As a user alters his or her processing parameter selections, that data may be added to the database 522 for use by the parameter set learning application 526 to be used to form a consensus processing parameter sets.

The memory/storage 520 may include a master parameter memory 528 to store all processing parameter sets and associated indicators currently used within the sound processing system 100. New or revised processing parameter sets developed by the parameter set learning application 526 may be stored in the master parameter memory 528. Some or all of the processing parameter sets stored in the master parameter memory 528 may be downloaded via the communications interface 540 to each of the plurality of personal audio systems in the sound processing system 100. For example, new or recently revised processing parameter sets may be pushed to some or all of the personal audio systems as available. Processing parameters sets, including new and revised processing parameter sets may be downloaded to some or all of the personal audio systems at periodic intervals. Processing parameters sets, including new and revised processing parameter sets may be downloaded upon request from individual personal audio systems.

Description of Processes

FIG. 6 shows flow charts of methods 600 and 700 for processing sound using collective feedforward. The methods 600 and 700 may be performed by a sound processing system, such as the sound processing system 100 (FIG. 1). The sound processing system may include a large plurality of personal audio systems, each having characteristics as akin to the personal audio system 140 (FIG. 1). The method 700 will, in most cases, produce better results with scale, so having more personal audio systems will usually be better. Thus, having thousands of personal audio systems, or even millions, may produce superior benefits.

The method 600 may be performed by each personal audio system concurrently but not necessarily synchronously. The method 700 may be performed by the sound knowledgebase concurrently with the method 600. All or portions of the methods 600 and 700 may be performed by hardware, by software running on one or more processors, or by a combination of hardware and software. All or portions of the method 600 may be performed by an active acoustic filter, such as the active acoustic filter 200, or may be distributed between an active acoustic filter and a personal computing device, such as the personal computing device 120. Although shown as a series of sequential actions for ease of discussion, the actions from 710 to 750 may occur continuously and simultaneously, and that the actions from 610 to 660 may be performed concurrently by the plurality of personal audio systems. Further, in FIG. 6, process flow is indicated solid arrows and information flow is indicated by dashed arrows.

The method 600 may start at 605 and run continuously until stopped (not shown). At 610, one or more processing parameter sets and associated indicators may be stored in a parameter memory, such as the dataset memory 430, within the personal audio system. Initially, one or more processing parameter sets may be stored in the personal audio system during manufacture or during installation of a personal audio system application on a personal computing device. Subsequently, new and/or revised processing parameter sets and associated indicators may be received from the sound knowledgebase.

At 620, an ambient audio stream derived from ambient sound may be processed in accordance with an active processing parameter set selected from the processing parameters sets stored at 610. Processes that may be performed at 620 were previous described. Concurrently with processing the ambient audio stream at 620, a most recent portion of the ambient audio stream may be stored in a snippet memory at 630, also as previously described.

At 640, a determination may be made whether or not a trigger event has occurred. Trigger events were previously described. When a determination is made at 640 that a trigger event has not occurred (“no” at 640), the processing at 620 and storing at 630 may continue. When a determination is made at 640 that a trigger event has occurred (“yes” at 640), a processing parameter set may be stored or retrieved at 650 as appropriate. At 650, a current processing parameter set, for example, as defined or edited by a user, may be stored in dataset memory 430. At 650, a previously stored processing parameter set may be retrieved from the dataset memory 430, either in repose to a user action or automatically, for example in response to a change in the ambient sound, user location or context.

At 660, feature data may be extracted from the most recent audio snippet. The audio snippet memory may be located within one or both active acoustic filters, or within a personal computing device, or may be distributed between the active acoustic filters and the personal computing device. Feature extraction may be performed by a processor within one or both active acoustic filters, a processor within the personal computing device, or may be distributed between the active acoustic filters and the personal computing device. Audio snippet data stored in one or both active acoustic filters may be transmitted to the personal computing device prior to feature extraction.

As previously described, data regarding a large number of different features can be extracted from an audio signal. Extracting all of the possible feature data from the most recent audio snippet may present an unreasonable or undesirable burden on the processor(s) within a personal audio system. Further only a subset of the possible feature data may be relevant to any given ambient sound environment.

To reduce the processing burden, an optional process 800, shown in FIG. 7, may be employed at 660 to preselect relevant feature data before some or all of the feature data is extracted. At 810, a preliminary analysis is performed on the most recent audio snippet. Alternately or additionally, at 820, a query may be sent, via a user interface, to a user to identify a sound (e.g. gunshot, breaking glass, automobile crash, etc.) captured in the most recent audio snippet. At 830, a subset of the possible audio feature data is selected as particularly relevant to the most recent audio snippet. The relevant audio feature data may be selected based on the results of the preliminary analysis at 810, a user response to the query at 820, and/or the type of event detected at 640 (FIG. 6). At 840 the relevant audio feature data is extracted from the most recent audio snippet and subsequently transmitted to the sound knowledgebase 150.

The type of features and algorithms for extracting features at 840 and criteria for selecting relevant audio feature data at 830 may evolve over time. For example, the type of features, algorithms for extracting features, and criteria for selecting relevant audio feature data may be defined by the knowledgebase and transmitted to the personal audio system as updates to firmware and/or software for the processors in the active audio filters and/or the personal computing device.

Referring once again to FIG. 6, at 670 the extracted feature data and associated metadata may be transmitted or uploaded to the sound knowledgebase. The uploaded metadata may include a location of the personal audio system provided by a geo-locator within the personal audio system. When the trigger event was a user-initiated or automatic retrieval of a selected processing parameter set from the parameter memory, the uploaded metadata may include an identifier of the selected processing parameter set and/or the actual selected processing parameter set. When the trigger event was the user modifying the active processing parameter or creating a new processing parameter set, the uploaded metadata may include the modified or new processing parameter set. Further, the user may be prompted to enter a context, descriptor, or other tag to be associated with the modified or new processing parameter set and uploaded.

The process 600 may take place over time, such that uploads of each set of feature data and metadata at 670 may take place periodically, for example, with feature data and metadata uploaded at 670 daily, weekly, or on longer intervals. However, the process 600 may take place substantially instantaneously such that newly-stored audio snippets at 630 and extracted feature data at 660 are uploaded mere seconds or fractions of seconds after associated ambient audio is received and without interrupting any ongoing processing at 620 This may enable the sound knowledgebase process, described below, to take place in substantially the same time and to, thereby, suggest new datasets for use by the personal audio system as new sounds are being heard and characterized.

The process 600 may then return to 620 and continue cyclically until stopped.

At 710, the sound knowledgebase receives the feature data and associated metadata transmitted at 670 and may receive additional feature data and metadata from other personal audio systems. Analysis may be performed on the received feature data at 720. The audio analysis at 720 may develop additional data about the features and characteristics of the ambient audio at the personal audio system. The additional data developed by the audio analysis at 720 may be stored in a database at 730 in association with the corresponding audio feature data and metadata received at 710.

At 740, machine learning techniques may be applied to learn revised and/or new processing parameter sets from the feature data, additional data, and metadata stored in the database 730. A variety of analytical techniques may be used to learn revised and/or new processing parameter sets. These analytical techniques may include, for example, numerical and statistical analysis of feature data, additional data, and metadata such as location, date, and time metadata. These analytical techniques may include, for further example, semantic analysis of tags, descriptors, contexts, and other non-numerical metadata. Similar to the near instantaneous processing and transmission performed by the personal audio system(s), when new processing parameter sets are learned at 740, they may immediately be downloaded at 750 by one or more personal audio systems.

As an example of a learning process that may be performed at 740, some or all of the records in the database at 730 may be sorted into a plurality of clusters based according to feature data, location, tag or descriptor, or some other factor. Some or all of these clusters may optionally be sorted into sub-clusters based on another factor. When records are sorted into clusters or sub-clusters based on non-numerical metadata (e.g. tags or descriptors) semantic analysis may be used to combine like metadata into a manageable number of clusters or sub-clusters. A consensus processing parameter set may then be developed for each cluster or sub-cluster. For example, clear outliers may be discarded and the consensus processing parameter set may be formed from the medians or means of processing parameters within the remaining processing parameter sets.

New or revised processing parameter sets learned and stored at 740 may be transmitted to some or all of the plurality of personal audio systems at 750. For example, new or recently revised processing parameter sets may be pushed to some or all of the personal audio systems on an as-available basis, which is to say as soon as the new or recently revised processing parameter sets are created. Processing parameters sets, including new and revised processing parameter sets may be transmitted to some or all of the personal audio systems at predetermined periodic intervals, such as, for example, nightly, weekly, or at some other interval. Processing parameters sets, including new and revised processing parameter sets may be transmitted upon request from individual personal audio systems. Processing parameter sets may be pushed to, or downloaded by, a personal audio system based on a change in the location of the personal audio system. For example, a personal audio system that relocates to a position near or in an airport may receive one or more processing parameters sets for use suppressing aircraft noise.

FIG. 8 shows flow charts of methods 900 and 1000 for processing sound using collective feedforward akin to those shown in FIG. 6. The method 900 may start at 905 and run continuously until stopped (not shown). The actions at 910 to 970 are the same as the corresponding actions (610 to 650) in the process 600 as shown in FIG. 6. Descriptions of these actions will not be repeated.

The actions 1010 to 1030 of the process 1000 are similar to the corresponding actions within the process 700 shown in FIG. 6, with the exception that the process 1000 receives, analyzes, accesses a database, then transmits new dataset(s) to one or more personal audio systems.

In this process, no new learning is made, at least not initially. The process 1000 can take place substantially simultaneously with the process 700 in FIG. 6. Here, however, only previously-stored and previously-learned data is used so that new processing parameter set(s) may be quickly provided from the sound knowledgebase to one or more personal audio systems.

Accessing the database at 1030 is merely utilizing the feature data (e.g. the received audio snipped, feature data and/or metadata) analyzed at action 1020 to determine what type of audio is presently being received by the personal audio system. This feature data may also include location data (or a near-future location derived from calendar, contact, email, or text data resident on a personal computing device).

Those processing parameter set(s) that are most-relevant to the feature data are identified while accessing the database at 1030 and, subsequently, are transmitted at 1040 to personal audio system(s) that are presently receiving audio characterized by the extracted feature data. The processes 900 and 1000 may continue cyclically until stopped.

The overall process of learning new or revised processing parameter sets based on audio snippets and metadata and providing those new or revised processing parameter sets to personal audio systems is referred to herein as “collective feedforward”. The term “collective” indicates the new or revised processing parameter sets are learned from the collective inputs from multiple personal audio systems. The term “feedforward” (in contrast to “feedback”) indicates new or revised processing parameter sets are provided, or fed forward, to personal audio systems that may not have contributed snippets and metadata to the creation of those new or revised processing parameter sets.

Information collected by the sound knowledgebase about how personal audio systems are used in different locations, ambient sound environments, and situations may be useful for more than developing new or revised processing parameter sets. In particular, information received from users of personal audio systems may indicate a degree of satisfaction with an ambient sound environment. For example, information may be collected from personal audio systems at a concert to gauge listener satisfaction with the “house” sound. If all or a large portion of the personal audio systems were used to substantially modify the house sound, a presumption may be made that the audience (those with and without personal audio systems) was not satisfied. Information received from personal audio systems could be used similarly to gauge user satisfaction with the sound and noise levels within stores, restaurants, shopping malls, and the like. Information received from personal audio systems could also be used to create soundscapes or sound level maps that may be helpful, for example, for urban planning and traffic flow engineering.

Closing Comments

Throughout this description, the embodiments and examples shown should be considered as exemplars, rather than limitations on the apparatus and procedures disclosed or claimed. Although many of the examples presented herein involve specific combinations of method acts or system elements, it should be understood that those acts and those elements may be combined in other ways to accomplish the same objectives. With regard to flowcharts, additional and fewer steps may be taken, and the steps as shown may be combined or further refined to achieve the methods described herein. Acts, elements and features discussed only in connection with one embodiment are not intended to be excluded from a similar role in other embodiments.

As used herein, “plurality” means two or more. As used herein, a “set” of items may include one or more of such items. As used herein, whether in the written description or the claims, the terms “comprising”, “including”, “carrying”, “having”, “containing”, “involving”, and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Only the transitional phrases “consisting of” and “consisting essentially of”, respectively, are closed or semi-closed transitional phrases with respect to claims. Use of ordinal terms such as “first”, “second”, “third”, etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed, but are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term) to distinguish the claim elements. As used herein, “and/or” means that the listed items are alternatives, but the alternatives also include any combination of the listed items. 

1. A learning personal audio system, comprising: a housing configured to interface with a user's ear; an input subsystem disposed within the housing and comprising a microphone, a preamplifier, and an analog to digital converter coupled to one another, the input subsystem configured to convert ambient sound into digitized ambient sound; a network interface for receiving one or more sound profiles each with a respective sets of processing parameters from a remote server; a memory for storing one or more of the sound profiles received using the network interface; and a processor in communication with the input subsystem, the network interface, and the memory, the processor configured to characterize the digitized ambient sound so as to generate feature data for the digitized ambient sound, instruct the network interface to transmit the feature data to the remote server, request one or more appropriate sound profiles from the remote server based upon the feature data, receive one or more selected sound profiles, selected by the remote server based upon the feature data, and initiate processing of the digitized ambient sound based upon the one or more selected sound profiles received from the remote server to generate digitized processed sound; and an output subsystem disposed within the housing and in communication with the processor, the output subsystem comprising a digital to analog converter, an amplifier and a speaker coupled to one another, the output subsystem configured to convert the digitized processed sound into processed output sound for the user's ear; wherein the one or more selected sound profiles are based upon processing parameters which are manually selected by a plurality of other users of personal audio systems similar to the learning personal audio system.
 2. The learning personal audio system of claim 1 further comprising: a geo-location engine in communication with the processor configured to determine a current location for the learning personal audio system; wherein the feature data further includes the current location; and the one or more selected sound profiles are also based upon the current location of the learning personal audio system.
 3. The learning personal audio system of claim 2 wherein the current location is used to select the one or more selected sound profiles based upon sound profiles manually selected by other users of similar personal audio systems while those other users are near the current location.
 4. The learning personal audio system of claim 2 wherein the one or more selected sound profiles are selected based upon location type, as defined by a categorization made by the remote server comparing a location type of the current location to a database of potential location types, and based upon sound profiles manually selected by other users of similar personal audio systems while those other users are near the current location.
 5. The learning personal audio system of claim 4 wherein the location type is one selected from the group comprising a stadium, a concert hall, a basketball court, an outdoor concert venue, a restaurant, an auditorium, an amphitheater, a classroom, a running trail, and an athletic field.
 6. The learning personal audio system of claim 1 wherein the one or more selected sound profiles are also selected using a context.
 7. The learning personal audio system of claim 1 wherein the one or more sound profiles are selected based upon a median one or more sound profiles selected by the plurality of other users.
 8. The learning personal audio system of claim 1 wherein the one or more sound profiles are selected based upon an average one or more sound profiles selected by the plurality of other users.
 9. A method for processing personal audio based using forward feedback, comprising: converting ambient sound into digitized ambient sound using an input subsystem disposed within a housing, the input subsystem comprising a microphone, a preamplifier, and an analog to digital converter coupled to one another; receiving, via a network interface, one or more sound profiles each with a respective sets of processing parameters from a remote server; characterizing the digitized ambient sound so as to generate a feature datafor the digitized ambient sound; instructing the network interface to transmit the feature data to the remote server; requesting one or more appropriate sound profiles from the remote server based upon the feature data; receiving one or more selected sound profiles, selected by the remote server based upon the feature data; storing, in a memory, one or more of the sound profiles received using the network interface; initiating processing of the digitized ambient sound based upon the one or more selected sound profiles received from the remote server to generate digitized processed sound; converting the digitized processed sound into processed output sound for the user's ear using an output subsystem disposed within the housing, the output subsystem comprising a digital to analog converter, an amplifier and a speaker coupled to one another; and wherein the one or more selected sound profiles are based upon processing parameters which are manually selected by a plurality of other users of personal audio systems similar to the learning personal audio system.
 10. The method of claim 9 further comprising: determining a current location for the learning personal audio system using a a geo-location engine; wherein the feature data further includes the current location; and the one or more selected sound profiles are also based upon the current location of the learning personal audio system.
 11. The method of claim 10 wherein the current location is used to select the one or more selected sound profiles based upon sound profiles manually selected by other users of similar personal audio systems while those other users are near the current location.
 12. The method of claim 10 wherein the one or more selected sound profiles are selected based upon location type, as defined by a categorization made by the remote server comparing a location type of the current location to a database of potential location types, and based upon sound profiles manually selected by other users of similar personal audio systems while those other users are near the current location.
 13. The method of claim 12 wherein the location type is one selected from the group comprising a stadium, a concert hall, a basketball court, an outdoor concert venue, a restaurant, an auditorium, an amphitheater, a classroom, a running trail, and an athletic field.
 14. The method of claim 9 wherein the one or more selected sound profiles are also selected using a context.
 15. The method of claim 9 wherein the one or more sound profiles are selected based upon a median one or more sound profiles selected by the plurality of other users.
 16. The method of claim 9 wherein the one or more sound profiles are selected based upon an average one or more sound profiles selected by the plurality of other users.
 17. A learning personal audio system, comprising: a housing configured to interface with a user's ear; an input subsystem disposed within the housing and comprising a microphone, a preamplifier, and an analog to digital converter coupled to one another, the input subsystem configured to convert ambient sound into digitized ambient sound; a network interface for transmitting feature data for use in generating one or more sound profiles each with a respective sets of processing parameters by a remote server; a memory for storing digitized ambient sound and feature data; and a processor in communication with the input subsystem, the network interface, and the memory, the processor configured to characterize the digitized ambient sound so as to generate feature data for the digitized ambient sound, capture user manual selection data comprising data identifying the selection of at least one set of processing parameters associated with the digitized ambient sound, instruct the network interface to transmit the feature data and selection data to the remote server for use by the remote server in identifying approporiate sound profiles for use by personal audio systems in processing sound with features similar to those identified by the feature data.
 18. The learning personal audio system of claim 17 further comprising: a geo-location engine in communication with the processor configured to determine a current location for the learning personal audio system; and wherein the network interface is further instructed to transmit the current location along with the feature data and the selection data.
 19. A learning personal audio system, comprising: a housing configured to interface with a user's ear; an input subsystem disposed within the housing and comprising a microphone, a preamplifier, and an analog to digital converter coupled to one another, the input subsystem configured to convert ambient sound into digitized ambient sound; a network interface for receiving one or more sound profiles each with a respective sets of processing parameters from a remote server; a memory for storing one or more of the sound profiles received using the network interface; and a processor in communication with the input subsystem, the network interface, and the memory, the processor configured to request one or more appropriate sound profiles from the remote server based upon feature data of the digitized ambient sound, receive one or more selected sound profiles, selected by the remote server based upon the feature data, and initiate processing of the digitized ambient sound based upon the one or more selected sound profiles received from the remote server to generate digitized processed sound; and an output subsystem disposed within the housing and coupled to the processor, the output subsystem comprising a digital to analog converter, an amplifier and a speaker coupled to one another, the output subsystem configured to convert the digitized processed sound into processed output sound for the user's ear; wherein the one or more selected sound profiles are based upon processing parameters which are manually selected by a plurality of other users of personal audio systems similar to the learning personal audio system.
 20. The learning personal audio system of claim 19 further comprising: a geo-location engine in communication with the processor configured to determine a current location for the learning personal audio system; and wherein the network interface is further instructed to transmit the current location along with the feature data. 